Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers

E I Elsedimy,Fahad Algarni,Miin-Shen Yang

doi:10.1155/2021/8892734

Abstract

Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider’s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms.

Highlights

Cloud computing provides a promising approach through which pools of services are delivered to users over the Internet [1]
We compare the performance of the proposed algorithm with three competing algorithms including a traditional single-objective algorithm FDD [52] and two multiobjective algorithms MGGA [19] and VMPACS [20] in terms of energy consumption, Service Level Agreements (SLAs) violation, and resource wastage
VMPMOPSO treats the problem of Virtual Machine Placement (VMP) as a multiobjective optimization problem and uses Multiobjective Particle Swarm Optimization (MOPSO) to optimally solve the VMP problem. e proposed approach optimizes multiobjectives such as SLA violations, power consumption, and resource wastage

Summary

Introduction

Cloud computing provides a promising approach through which pools of services are delivered to users over the Internet [1]. To meet the high-performance expectations of cloud users in a cost-effective manner, cloud providers must consider several key challenges, such as large power consumption, high resource wastage, and high Service Level Agreement (SLA) violation [3] In this context, energy management in cloud data centers has been a crucial issue since it has a direct effect on operating costs [3]. Several techniques have been applied to address the problem of VMP in virtualized data center’s infrastructure, such as Linear Programming (LP) [12], random greedy algorithm [13], heuristic bin packing [14], simulated annealing optimization [15], constraint programming [16], Genetic Algorithm (GA) [17], and a classical Ant Colony Optimization (ACO) algorithm [18] Another approach to optimize VM placement is achieved based on a generalization of the Knapsack Problem [19].

Related Work

An Introduction to the Multiobjective Improved PSO

The Proposed VMPMOPSO Approach

Results and Discussion

10: Update E

Conclusions